Characterizing the Instrumental Profile of LAMOST

TL;DR

This paper uses neural networks to model the instrumental profile of LAMOST, improving wavelength calibration and stellar radial velocity measurements, aiding binary star detection.

Contribution

It introduces a neural network-based method to accurately derive LAMOST's instrumental profile for enhanced spectroscopic analysis.

Findings

Neural network model effectively retrieves the IP for any fiber and wavelength.

Applying the derived IP reduces RV measurement dispersion by about 3 km/s.

Improved RV precision facilitates long-period binary star searches.

Abstract

The instrumental profile (IP) of a telescope is of great significance for spectroscopic analyses, especially for wavelength calibration and stellar parameter measurements. The Large Sky Area Multi-Object Fiber Spectroscopic Telescope (LAMOST) employs arc lamps for wavelength calibration. These lamps produce sharp emission lines with known wavelengths, and the observed arc lamp spectra can well characterize the IP. However, IPs are influenced by multiple factors, making them difficult to model accurately with traditional methods. Neural networks, which can automatically capture complex patterns and nonlinear features in data, provide a promising approach for high-precision IP measurement. We therefore construct a multi-layer perceptron (MLP) based on The Payne neural network to derive IPs for LAMOST. After training, the model can retrieve the IP for any fiber, at any wavelength, and at any time. We then apply the derived IP to stellar radial velocity (RV) measurements and analyze the impact of different IP center localization methods on the results. Finally, the dispersion of the measured RVs is reduced by approximately 3 km/s. This improvement will facilitate the search for long-period binary stars via RV variations.